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Groundwater quality assessment with respect to fuzzy water quality index (FWQI): an application of expert systems in environmental monitoring

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Abstract

Water quality degradation affects socio-economic development inappropriately and has dire effects on human health too. Water quality indexes (WQIs) are the methods widely used for modelling water quality status. However, using these indexes is limited by some constraints like deficiency of necessary database or uncertainty of decision-making. Throughout the ongoing research, fuzzy water quality indexes (FWQIs) were developed based on the Mamdani fuzzy inference system (FIS) to overcome the above-mentioned limitations. In other words, seven FWQIs models with different water quality parameters have been developed based on triangular and trapezoidal membership functions. Later, the developed indexes were employed to evaluate the water quality of 17 wells in Saveh Plain, Iran. Compared to the conventional WQI, the results showed that the elimination of some needed parameters in development of FWQI did not decrease the accuracy of water quality classification. However, omitting some other parameters with undesirable values made the classification of water quality unreliable. According to the results, some 35 % of wells benefitted from proper drinking water quality, while approximately 30 and 35 % of them suffered from unsuitable and very poor drinking water quality, respectively.

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References

  • Araghinejad S (2014) Data-driven modeling: using MATLAB in water resources and environmental engineering. Springer, New York

    Book  Google Scholar 

  • Aryafar A, Yousefi S, Doulati Ardejani F (2013) The weight of interaction of mining activities: groundwater in environmental impact assessment using fuzzy analytical hierarchy process (FAHP). Environ Earth Sci 68(8):2313–2324

    Article  Google Scholar 

  • Azarnivand A, Hashemi-Madani FS, Banihabib ME (2014) Extended fuzzy analytic hierarchy process approach in water and environmental management (case study: Lake Urmia Basin, Iran). Environ Earth Sci. doi:10.1007/s12665-014-3391-6

    Google Scholar 

  • Backman B, Bodiš D, Lahermo P, Rapant S, Tarvainen T (1998) Application of a groundwater contamination index in Finland and Slovakia. Environ Geol 36(1–2):55–64

    Article  Google Scholar 

  • Boyacioglu H (2007) Development of a water quality index based on a European classification scheme. Water SA 33:101–106

    Google Scholar 

  • Casper M, Gemmar P, Gronz O, Johst M, Stuber M (2007) Fuzzy logic-based rainfall-runoff modelling using soil moisture measurements to represent system state. Hydrol Sci J 52(3):478–490

    Article  Google Scholar 

  • Chang F-J, Chang Y-T (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10

    Article  Google Scholar 

  • Chang N, Chen HW, Ning SK (2001) Identification of river water quality using the fuzzy synthetic evaluation approach. J Environ Manage 63:293–305

    Article  Google Scholar 

  • Dahiya S, Singh B, Gaur S, Garg VK, Kushwaha HS (2007) Analysis of groundwater quality using fuzzy synthetic evaluation. J Hazard Mater 147(3):938–946

    Article  Google Scholar 

  • Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374:235–241

    Article  Google Scholar 

  • Fleming SW, Wong C, Graham G (2014) The unbearable fuzziness of being sustainable: an integrated, fuzzy logic-based aquifer health index. Hydrol Sci J 59(6):1154–1166

    Article  Google Scholar 

  • Gharibi H, Mahvi AH, Nabizadeh R, Arabalibeik H, Yunesian M, Sowlat MH (2012) A novel approach in water quality assessment based on fuzzy logic. J Environ Manage 112:87–95

    Article  Google Scholar 

  • Icaga Y (2007) Fuzzy evaluation of water quality classification. Ecol Ind 7(3):710–718

    Article  Google Scholar 

  • Isalou AA, Zamani V, Shahmoradi B, Alizadeh H (2013) Landfill site selection using integrated fuzzy logic and analytic network process (F-ANP). Environ Earth Sci 68:1745–1755

    Article  Google Scholar 

  • Kung H, Ying L, Liu L (1992) Complementary tool to water quality index: fuzzy clustering analysis. Water resour bull 28(3):525–533

    Article  Google Scholar 

  • Lermontov A, Yokoyama L, Lermontov M, Machado MAS (2009) River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil. Ecol Indic 9(6):1188–1197

    Article  Google Scholar 

  • Lu RS, Lo SL, Hu JY (1999) Analysis of reservoir water quality using fuzzy synthetic evaluation. Stoch Environ Res Risk Assess 13:327–336

    Article  Google Scholar 

  • Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controllers. Int J Man Mach Stud 8(6):669–678

    Article  Google Scholar 

  • Milovanovic M (2007) Water quality assessment and determination of pollution sources along the Axios/Vardar River, South-eastern Europe. Desalination 213(1):159–173

    Article  Google Scholar 

  • Moghaddamnia A, GhafariGousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97

    Article  Google Scholar 

  • Nasiri F, Maqsood I, Huang G, Fuller N (2007) Water quality index: a fuzzy river-pollution decision support expert system. J Water Resour Plan Manag 133(2):95–105

    Article  Google Scholar 

  • Ocampo-Duque W, Ferre-Huguet N, Domingo JL, Schuhmacher M (2006) Assessing water quality in rivers with fuzzy inference systems: a case study. Environ Int 32(6):733–742

    Article  Google Scholar 

  • Ren L, Xiang X-Y, Ni J-J (2013) Forecast modeling of monthly runoff with adaptive neural fuzzy inference system and wavelet analysis. J Hydrol Eng 18(9):1133–1139

    Article  Google Scholar 

  • Rizwan R, Gurdeep S (2010) Assessment of ground water quality status by using water quality index method in Orissa, India. World Appl Sci J 9(12):1392–1397

    Google Scholar 

  • Sadat-Noori SM, Ebrahimi K, Liaghat AM (2013) Groundwater quality assessment using the water quality index and GIS in Saveh-Nobaran aquifer, Iran. Environ Earth Sci 71(9):3827–3843

    Article  Google Scholar 

  • Saeedi M, Abessi O, Sharifi F, Meraji H (2010) Development of groundwater quality index. Environ Monit Assess 163(1–4):327–335

    Article  Google Scholar 

  • Said A, Stevens D, Selke G (2004) An innovative index for evaluating water quality in streams. Environ Manage 34:406–414

    Article  Google Scholar 

  • Sasikumar K, Mujumdar PP (1998) Fuzzy optimization model for water quality management of a river system. J water Resour Plan Manag 124(2):79–88

    Article  Google Scholar 

  • Sasikumar K, Mujumdar PP (2010) Application of fuzzy probability in water quality management of a river system. Int J Syst Sci 31(5):575–591

    Article  Google Scholar 

  • Scannapieco D, Naddeo V, Zarra T, Belgiorno V (2012) River water quality assessment: a comparison of binary-and fuzzy logic-based approaches. Ecol Eng 47:132–140

    Article  Google Scholar 

  • Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43

    Article  Google Scholar 

  • Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Inc, Philippines

    Google Scholar 

  • Terzi O, Keskin ME, Taylan ED (2006) Estimating evaporation using ANFIS. J Irrig Drain Eng 132(5):503–507

    Article  Google Scholar 

  • Van Broekhoven E, De Baets B (2006) Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets Syst 157(7):904–918

    Article  Google Scholar 

  • Wang K-H, Altunkaynak A (2012) a comparative case study of rainfall-runoff modeling between SWMM and FUZZY logic approach. J Hydrol Eng 17(2):283–291

    Article  Google Scholar 

  • WHO (2004) Guidelines for drinking water quality: training pack. WHO, Geneva

    Google Scholar 

  • Yager RR, Filev DP (1994) Essentials of fuzzy modeling and control. Wiley, New York

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to University of Tehran along with the Regional Water Corporation of Markazi (Arak) Province, Iran, for providing data and facilities related to conducting the present research.

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Correspondence to Kumars Ebrahimi.

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Hosseini-Moghari, SM., Ebrahimi, K. & Azarnivand, A. Groundwater quality assessment with respect to fuzzy water quality index (FWQI): an application of expert systems in environmental monitoring. Environ Earth Sci 74, 7229–7238 (2015). https://doi.org/10.1007/s12665-015-4703-1

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